Electron Density Reconstruction by Ionospheric Tomography From the Combination of GNSS and Upcoming LEO Constellations

Computerized ionospheric tomography (CIT) based on Global Navigation Satellite System (GNSS) data is a worthwhile project. However, predominant issues like unevenly distributed stations, lack of data, and high-elevation GNSS signals could affect the precise information about the ionosphere. To address this problem, many previous studies were mainly improving tomographic algorithms, rather than incorporating multisource data such as data from rapidly developed upcoming Low-Earth-Orbit (LEO) satellites. In this paper, the potential improvements of GNSS-based ionospheric tomography at a global scale by combining the upcoming LEO constellations is investigated. On this basis, some numerical experiments were conducted to evaluate the performances of LEO-augmented GNSS ionospheric tomography. Specifically, the slant total electron contents (STEC) required for ionospheric tomography from ground receivers to GNSS (including GPS, GLONASS, BDS, and Galileo) and LEO satellites are simulated by the international reference ionosphere (IRI-2016) model during low and high solar activity in 2018 and 2014, respectively. The simulated STEC values from single-, double- and quad systems of GNSS constellations were first used for ionospheric tomography. Afterward, the GNSS-based STEC values were combined with the LEO-based simulated STEC values. The STEC values directly derived from the IRI-2016 model that have not been used in ionospheric tomography were used as independent references. The results showed that the GNSS + LEO solutions outperform the GNSS-only solutions overall, the STEC values obtained by GNSS + LEO solutions are more consistent with the STEC references during both high and low years of solar activity. In comparison with the STEC references, the RMS values of the CIT-derived STEC for GNSS-only solutions can be decreased by 15% and 20.3% at most during low and high years of solar activity, respectively.